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Automobile Engine Fault Judgment Method and Device Based on Voice Recognition

A technology for automobile engine and voice recognition, applied in neural learning methods, character and pattern recognition, internal combustion engine testing, etc., can solve problems such as inaccurate independent judgment, inability to use engine operating status information, re-fault judgment, etc.

Active Publication Date: 2020-10-27
BEIJING MECHANICAL EQUIP INST
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0004] In view of the above analysis, the embodiment of the present invention aims to provide a method for judging automobile engine faults based on voice recognition, which is used to solve the problem of inaccurate autonomous judgment of faults in the prior art, the inability to use engine operating state information in the judgment process, and the need to re-fault after subsequent maintenance. Judgment issue

Method used

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  • Automobile Engine Fault Judgment Method and Device Based on Voice Recognition
  • Automobile Engine Fault Judgment Method and Device Based on Voice Recognition
  • Automobile Engine Fault Judgment Method and Device Based on Voice Recognition

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Embodiment 1

[0066] A specific embodiment of the present invention discloses a method for judging automobile engine faults based on sound recognition, such as figure 1 shown, including the following steps:

[0067] S1. Collect the real-time monitoring sound data of the car engine through the sound receiving device.

[0068] S2. Perform time-frequency two-dimensional processing on the real-time monitoring sound data to obtain a time-frequency two-dimensional signal corresponding to the sound of the automobile engine.

[0069] S3. Input the time-frequency two-dimensional signal into the trained hybrid neural network, and judge whether the automobile engine is faulty and the specific fault location according to the output result of the hybrid system; the hybrid neural network includes AlexNet and LSTM. If yes, give an alarm and display the specific fault location; if not, display no fault.

[0070] In this embodiment, the existing AlexNet includes 5 convolutional layers for feature extracti...

Embodiment 2

[0073] Optimizing on the basis of the above examples, such as image 3 As shown, the steps of training the hybrid neural network include:

[0074] S01. Get includes N 1 Group automobile engine failure sound data and corresponding engine state, the training set of fault type; Said engine state comprises acceleration, deceleration, constant speed, and said training set should include all possible fault types of automobile engine.

[0075] S02. Perform time-frequency two-dimensional processing on each group of car engine sound data, and obtain time-frequency two-dimensional signals corresponding to each group of car engine sound.

[0076] S03. Input the time-frequency two-dimensional signal and fault type corresponding to each group of car engine sounds into AlexNet for training, and at the same time, input the time-frequency two-dimensional signal and engine state corresponding to each group of car engine sounds into LSTM for training Train to get a trained hybrid neural netwo...

Embodiment 3

[0099] A kind of automobile engine fault judging device that adopts the automobile engine fault judging method described in embodiment 2 to judge, such as Figure 7 As shown, it includes an audio collection device, a voice recognition device and a central control display device connected in sequence.

[0100] The audio collection device is used to collect the real-time monitoring sound data of the automobile engine, perform time-frequency two-dimensional processing on the real-time monitoring sound data, and input the time-frequency two-dimensional signal corresponding to the obtained automobile engine sound into the sound recognition device.

[0101] The sound recognition device is used to input the received time-frequency two-dimensional signal into the trained hybrid neural network, and judge whether the automobile engine fails and the specific fault location according to the output result of the hybrid neural network; the hybrid neural network Including AlexNet, LSTM.

[...

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Abstract

The invention relates to a sound recognition-based automobile engine fault judgment method and its device, which belong to the technical field of engine fault detection and solve the problems of inaccurate fault autonomous judgment in the prior art, inability to use engine operating state information in the judgment process, and re-faults required for subsequent maintenance. issue of judgment. The automotive engine fault judgment method disclosed in the present invention adopts a hybrid neural network, performs fault recognition according to AlexNet, and uses LSTM to identify engine running time status to assist more accurate fault judgment, and the layers of the AlexNet and LSTM neural networks used are more than 9 layers , it has been verified by experiments that its recognition accuracy is high, and the judgment result is very accurate. In actual application, the automobile engine fault judging method of the present invention can simply, quickly and reliably judge the common faults that occur in the automobile engine, eliminate them in time, and do not need to re-judgment the fault for subsequent maintenance.

Description

technical field [0001] The invention relates to the technical field of engine fault detection, in particular to a sound recognition-based automobile engine fault judgment method and a device thereof. Background technique [0002] At present, in the daily use of automobiles, due to the influence of natural and human factors, the engine is prone to problems such as exhaust pipe failure, ignition system failure, throttle sticking, and crankshaft main bearing failure. Usually, a car engine is equipped with a fault detection device, and the instrument panel can display whether the engine is faulty, but the existing technology cannot specify the specific fault location and fault type, and the user cannot judge whether to continue driving. At the same time, it also increases the difficulty of engine maintenance. Increased consumption of manpower and material resources. [0003] A car engine failure generally causes the sound to change when it is working. However, in the actual dri...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06N3/08G06K9/00G06F17/14G01M15/04
CPCG06F17/14G06N3/084G01M15/04G06F2218/00
Inventor 张驰
Owner BEIJING MECHANICAL EQUIP INST
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